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import os |
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from typing import List |
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import numpy as np |
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import pooch |
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from PIL import Image |
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from PIL.Image import Image as PILImage |
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from .base import BaseSession |
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class U2netHumanSegSession(BaseSession): |
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""" |
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This class represents a session for performing human segmentation using the U2Net model. |
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""" |
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def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]: |
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""" |
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Predicts human segmentation masks for the input image. |
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Parameters: |
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img (PILImage): The input image. |
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*args: Variable length argument list. |
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**kwargs: Arbitrary keyword arguments. |
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Returns: |
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List[PILImage]: A list of predicted masks. |
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""" |
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ort_outs = self.inner_session.run( |
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None, |
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self.normalize( |
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img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225), (320, 320) |
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), |
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) |
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pred = ort_outs[0][:, 0, :, :] |
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ma = np.max(pred) |
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mi = np.min(pred) |
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pred = (pred - mi) / (ma - mi) |
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pred = np.squeeze(pred) |
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mask = Image.fromarray((pred * 255).astype("uint8"), mode="L") |
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mask = mask.resize(img.size, Image.Resampling.LANCZOS) |
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return [mask] |
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@classmethod |
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def download_models(cls, *args, **kwargs): |
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""" |
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Downloads the U2Net model weights. |
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Parameters: |
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*args: Variable length argument list. |
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**kwargs: Arbitrary keyword arguments. |
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Returns: |
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str: The path to the downloaded model weights. |
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""" |
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fname = f"{cls.name(*args, **kwargs)}.onnx" |
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pooch.retrieve( |
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"https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_human_seg.onnx", |
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( |
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None |
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if cls.checksum_disabled(*args, **kwargs) |
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else "md5:c09ddc2e0104f800e3e1bb4652583d1f" |
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), |
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fname=fname, |
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path=cls.u2net_home(*args, **kwargs), |
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progressbar=True, |
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) |
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return os.path.join(cls.u2net_home(*args, **kwargs), fname) |
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@classmethod |
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def name(cls, *args, **kwargs): |
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""" |
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Returns the name of the U2Net model. |
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Parameters: |
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*args: Variable length argument list. |
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**kwargs: Arbitrary keyword arguments. |
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Returns: |
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str: The name of the model. |
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""" |
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return "u2net_human_seg" |
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